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Lipidomics is an emerging and promising omic that analyzes different lipid molecules in a biological sample. It is considered as a branch of metabolomics, which is defined as the comprehensive analysis of metabolites in a biological specimen. Nonetheless, in recent years lipidomics is being considered a distinct discipline in the biomedicine field. Lipids play important roles in many biological pathways and could work as biomarkers of disease or as therapeutic targets for treatment diseases. The major lipidomics strategies are shotgun lipidomics and liquid chromatography coupled with mass spectrometry. Gastrointestinal diseases such as irritable bowel syndrome or inflammatory bowel disease are chronic diseases that need non-invasive biomarkers for prognosis and diagnosis. Even more, patients with inflammatory bowel disease are at a significantly increased risk of colorectal cancer, principally resulting from the pro-neoplastic effects of chronic intestinal inflammation. Current screening methods utilized globally include sigmoidoscopy, or standard colonoscopy but it is important to develop non-invasive and accurate screening tools to facilitate early detection and precise staging of colorectal cancer. Disease progression and response to treatment may benefit also from the application of these potential new tools. This review is focused on studies that use lipidomics approaches to discover potential biomarkers for monitoring the mentioned intestinal diseases and, particularly, tumour progression.

Circadian oscillations play a fundamental role in many biological processes including cell metabolism and cell cycle. As such, interest in understanding these molecular oscillations has generated an increasingly large collection of circadian omic data. These studies have demonstrated the remarkable plasticity with which the set of oscillating molecular species within a cell are selected. These large shifts in oscillating species are known as circadian reprogramming events. These events have been observed across experimental condition, tissue, and species. While many of these studies have made tissue or condition specific conclusions, a consolidated framework of software tools and a central repository of data has become necessary to answer questions about the orchestration of these reprogramming events.By combining the largest repository for circadian omic data with improved methods for the detection of circadian oscillation and regulation in omic studies, a model for the transcriptomic organization of circadian rhythms can be identified. Results are presented from a collection of specific reprogramming studies utilizing these new methods, as well as a high through-put analysis of a large collection of comparable transcriptomic datasets from mouse tissue. The identified model can be viewed as a deep hierarchical network of circadian regulation that originates from the core circadian clock to over 95% of oscillating transcripts.

Otitis media (OM) is an infective and inflammatory disorder known to be a major cause of hearing impairment across all age groups. Both acute and chronic OM result in substantial healthcare utilization related to antibiotic prescription and surgical procedures necessary for its management. Although several studies provided evidence of genetics playing a significant role in the susceptibility to OM, we had limited knowledge about the genes associated with OM until recently. Here we have summarized the known genetic factors that confer susceptibility to various forms of OM in mice and in humans and their genetic load, along with associated cellular signaling pathways. Spotlighted in this review are fucosyltransferase (FUT) enzymes, which have been implicated in the pathogenesis of OM. A comprehensive understanding of the functions of OM-associated genes may provide potential opportunities for its diagnosis and treatment.

Bioinformatics for Pollen. Pollen plays a key role in crop production, and its development is the most delicate phase in reproduction. Different metabolic pathways are involved in pollen development, and changes in the level of some metabolites, as well as responses to stress, are correlated with the reduction in pollen viability, leading consequently to a decrease in the fruit production. However, studies on pollen may be hard because gamete development and fertilization are complex processes that occur during a short window of time. The rise of the so-called -omics sciences provided key strategies to promote molecular research in pollen tissues, starting from model organisms and moving to increasing number of species. An integrated multi-level approach based on investigations from genomics, transcriptomics, proteomics and metabolomics appears now feasible to clarify key molecular processes in pollen development and viability. To this aim, bioinformatics has a fundamental role for data production and analysis, contributing varied and ad hoc methodologies, endowed with different sensitivity and specificity, necessary for extracting added-value information from the large amount of molecular data achievable. Bioinformatics is also essential for data management, organization, distribution and integration in suitable resources. This is necessary to catch the biological features of the pollen tissues and to design effective approaches to identifying structural or functional properties, enabling the modeling of the major involved processes in normal or in stress conditions. In this review, we provide an overview of the available bioinformatics resources for pollen, ranging from raw data collections to complete databases or platforms, when available, which include data and/or results from -omics efforts on the male gametophyte. Perspectives in the fields will also be described.

Autoimmune liver diseases (AILDs) are complex conditions, which arise from the interaction between a genetic susceptibility and unknown environmental triggers. They represent a relevant cause of liver failure and liver transplantation worldwide. As a testimony of our progress in understanding the biology of AILDs and the disease progression is the overall median survival which has increased over the last decade. However, there are still major challenges such as the lack of therapies and surveillance strategies in primary sclerosing cholangitis (PSC), the management and treatment of non-responders to first-line therapies in primary biliary cholangitis (PBC) and the need for tailoring immunosuppressive drugs in autoimmune hepatitis (AIH). The different disease course and treatment response in patients with AILDs might be related to a heterogeneous genetic background between individuals which translates in a heterogeneous clinical phenotype. Thus, it becomes essential to personalise management and treatment based on specific risk profiles, e.g. low-risk and high-risk, based on genetic and molecular signatures. It is now possible, thanks to the development of large-scale AILDs patient cohorts, that such diseases can be analysed using various high-throughput methods like gene expression profiling, next generation sequencing and other omics technologies to identify unique fingerprints based on which a personalised or tailor-made management and therapy can be developed. The final aim being to facilitate treatment decision-making that balances patient-specific risks and preferences. This is critical especially now with the current and forthcoming availability of more efficacious medications. To reach this point we need specific interventions such as creating bigger biobanks, sequencing more genomes and linking biological information to health-related data. We have already identified subsets of patients with different risk profiles among patients with PBC, PSC and AIH by using clinical tools such as liver histology, laboratory investigation and non-invasive methods. In this manuscript, we review the clinical features and investigations that already enable us to individualize the care of PBC patients and that might support the development of precision medicine (PM) in AILDs.

Despite advances in myocardial reperfusion therapies, acute myocardial ischaemia/reperfusion injury and consequent ischaemic heart failure represent the number one cause of morbidity and mortality in industrialized societies. Although different therapeutic interventions have been shown beneficial in preclinical settings, an effective cardioprotective or regenerative therapy has yet to be successfully introduced in the clinical arena. Given the complex pathophysiology of the ischaemic heart, large scale, unbiased, global approaches capable of identifying multiple branches of the signalling networks activated in the ischaemic/reperfused heart might be more successful in the search for novel diagnostic or therapeutic targets. High-throughput techniques allow high-resolution, genome-wide investigation of genetic variants, epigenetic modifications, and associated gene expression profiles. Platforms such as proteomics and metabolomics (not described here in detail) also offer simultaneous readouts of hundreds of proteins and metabolites. Isolated omics analyses usually provide Big Data requiring large data storage, advanced computational resources and complex bioinformatics tools. The possibility of integrating different omics approaches gives new hope to better understand the molecular circuitry activated by myocardial ischaemia, putting it in the context of the human ‘diseasome’. Since modifications of cardiac gene expression have been consistently linked to pathophysiology of the ischaemic heart, the integration of epigenomic and transcriptomic data seems a promising approach to identify crucial disease networks. Thus, the scope of this Position Paper will be to highlight potentials and limitations of these approaches, and to provide recommendations to optimize the search for novel diagnostic or therapeutic targets for acute ischaemia/reperfusion injury and ischaemic heart failure in the post-genomic era.

The extraordinary progress experienced by sequencing technologies and bioinformatics has made the development of omic studies virtually ubiquitous in all fields of life sciences nowadays. However, scientific attention has been quite unevenly distributed throughout the different branches of the tree of life, leaving molluscs, one of the most diverse animal groups, relatively unexplored and without representation within the narrow collection of well established model organisms. Within this Phylum, bivalve molluscs play a fundamental role in the functioning of the marine ecosystem, constitute very valuable commercial resources in aquaculture, and have been widely used as sentinel organisms in the biomonitoring of marine pollution. Yet, it has only been very recently that this complex group of organisms became a preferential subject for omic studies, posing new challenges for their integrative characterization. The present contribution aims to give a detailed insight into the state of the art of the omic studies and functional information analysis of bivalve molluscs, providing a timely perspective on the available data resources and on the current and prospective applications for the biomonitoring of harmful marine compounds.

Cancer omics data are exponentially created and associated with clinical variables, and important findings can be extracted based on bioinformatics approaches which can then be experimentally validated. Many of these findings are related to a specific class of non-coding RNA molecules called microRNAs (miRNAs) (post-transcriptional regulators of mRNA expression). The related research field is quite heterogeneous and bioinformaticians, clinicians, statisticians and biologists, as well as data miners and engineers collaborate to cure stored data and on new impulses coming from the output of the latest Next Generation Sequencing technologies.Here we review the main research findings on miRNA of the first 10years in colon cancer research with an emphasis on possible uses in clinical practice. This review intends to provide a road map in the jungle of publications of miRNA in colorectal cancer, focusing on data availability and new ways to generate biologically relevant information out of these huge amounts of data.